mirror of
https://github.com/pytorch/pytorch.git
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35f36363ec
This reverts commit 2ee6b97464d17fcf4c1fc67c29868fa30d0c16e1. Reverted https://github.com/pytorch/pytorch/pull/133113 on behalf of https://github.com/wanchaol due to looks like it break some internal type imports ([comment](https://github.com/pytorch/pytorch/pull/133113#issuecomment-2295670911))
429 lines
18 KiB
Python
429 lines
18 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates
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import contextlib
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import functools
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import logging
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import operator
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import warnings
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from typing import cast, Dict, List, Optional, Sequence, Tuple, TYPE_CHECKING
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import torch
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import torch.distributed as dist
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import torch.distributed._tensor.api as dtensor
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import torch.distributed._tensor.random as random
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from torch.distributed._tensor._op_schema import (
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_is_inplace_op,
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_is_out_variant_op,
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OpInfo,
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OpSchema,
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OutputSpecType,
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)
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from torch.distributed._tensor._redistribute import redistribute_local_tensor
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from torch.distributed._tensor._sharding_prop import ShardingPropagator
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from torch.distributed._tensor._tp_conv import (
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convolution_backward_handler,
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convolution_handler,
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)
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from torch.distributed._tensor._utils import try_find_mesh_from_args
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from torch.distributed._tensor.placement_types import DTensorSpec, Replicate, TensorMeta
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from torch.distributed._tensor.random import is_rng_supported_mesh
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if TYPE_CHECKING:
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from torch.distributed.device_mesh import DeviceMesh
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try:
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from torch.utils import _cxx_pytree as pytree
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except ImportError:
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from torch.utils import _pytree as pytree # type: ignore[no-redef]
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aten = torch.ops.aten
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logger = logging.getLogger(__name__)
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def decompose_handler(
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op_call: torch._ops.OpOverload,
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args: Tuple[object, ...],
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kwargs: Dict[str, object],
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) -> object:
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"""
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Decomposes a op to core ATen op, this handler is mostly here
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for inference mode usage where the ops are not core aten ops.
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"""
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r = op_call.decompose(*args, **kwargs)
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if r is not NotImplemented:
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return r
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else:
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raise RuntimeError("Decomposition failed")
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def is_same_size_handler(
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op_call: torch._ops.OpOverload,
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args: Tuple[object, ...],
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kwargs: Dict[str, object],
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) -> bool:
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lhs = cast(torch.Tensor, args[0])
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rhs = cast(torch.Tensor, args[1])
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return lhs.shape == rhs.shape
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class OpDispatcher:
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"""
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Op dispatching class instance to handle args/kwargs pre-processing (un-wrapping), sharding
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propagation, redistribute local args, local compute, and post-processing (re-wrapping). It
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also handles any op specific logic if necessary.
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NOTE: Given the runtime overhead of Tensor subclass (__torch_dispatch__), the OpDispatcher
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is designed to minimize the CPU overhead by using the tricks of proper unflattening, faster
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pytree if needed, and leveraging various caching mechanisms implemented in the sharding
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propagation and redistribute modules. The CPU overhead is critical to eager mode performance,
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one need to carefully measure the CPU overhead when making significant changes to the
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OpDispatcher and ShardingPropagator.
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"""
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def __init__(self) -> None:
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self.sharding_propagator = ShardingPropagator()
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self._random_ops = {
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aten.native_dropout.default,
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aten.normal_.default,
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aten.rand_like.default,
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aten.randn_like.default,
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aten.randint_like.default,
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aten.randint_like.low_dtype,
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aten.randint_like.low_dtype_out,
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aten.uniform_.default,
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aten.bernoulli.default,
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aten.bernoulli_.float,
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}
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self._custom_op_handlers = {
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aten.linear.default: decompose_handler,
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aten.is_same_size.default: is_same_size_handler,
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aten.convolution.default: convolution_handler,
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aten.convolution_backward.default: convolution_backward_handler,
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}
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# This flag is used internally to control whether we treat the torch.Tensor(non-DTensor)
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# as implicitly replicated or we throw error to user.
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# NOTE: It is EXTREMELY UNSAFE to turn this flag on by default so we intentionally leave
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# it as False by default.
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self._allow_implicit_replication = False
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def dispatch(
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self,
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op_call: torch._ops.OpOverload,
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args: Tuple[object, ...],
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kwargs: Dict[str, object],
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) -> object:
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"""
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Main dispatching logic
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"""
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# operators that does not need to go through sharding propagation
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if op_call in self._custom_op_handlers:
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return self._custom_op_handlers[op_call](op_call, args, kwargs) # type: ignore[operator]
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# extract local tensor and sharding infos to a OpInfo
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op_info = self.unwrap_to_op_info(op_call, args, kwargs)
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logger.debug("Dispatching op_call: %s", op_info.schema)
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self.sharding_propagator.propagate(op_info)
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output_sharding = op_info.output_sharding
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logger.debug("output_sharding for %s: %s", op_call, output_sharding)
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assert output_sharding is not None, "output sharding should not be None"
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mesh = op_info.mesh
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if mesh.get_coordinate() is not None:
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# computation that happens in the current rank of the mesh, normal case
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if output_sharding.needs_redistribute:
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# If sharding propagation decision needs redistribute, perform redistribute
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# on args first, which could potentially modify args (i.e. allgather certain arg)
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assert output_sharding.redistribute_schema is not None
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self.redistribute_local_args(
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op_info, output_sharding.redistribute_schema
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)
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local_tensor_args = (
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pytree.tree_unflatten(
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cast(List[object], op_info.local_args), op_info.args_tree_spec
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)
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if op_info.args_tree_spec
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else op_info.local_args
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)
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# run local op computation with potentially modified args/kwargs
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local_tensor_args = cast(Tuple[object, ...], local_tensor_args)
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if op_call in self._random_ops:
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if not random._rng_tracker and is_rng_supported_mesh(mesh):
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# Default to `OffsetBasedRNGTracker` if the parallelism API
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# did not already construct one
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random._rng_tracker = random.OffsetBasedRNGTracker(mesh.device_type)
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first_arg, first_local_arg = cast(dtensor.DTensor, args[0]), cast(
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torch.Tensor, local_tensor_args[0]
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)
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rng_context = (
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random._rng_tracker._distribute_region(first_arg._spec)
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if random._rng_tracker and not first_local_arg.is_meta
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else contextlib.nullcontext()
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)
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# For DTensor random operator, run it within a RNGTracker context to
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# ensure the random number generator is properly distributed.
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with rng_context:
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local_results = op_call(*local_tensor_args, **op_info.local_kwargs)
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else:
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# normal case, run local sharded op computation
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local_results = op_call(*local_tensor_args, **op_info.local_kwargs)
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else:
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# For a non-participating device (happens on rank that does not belong to
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# the device mesh), we do:
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# 1. if the return type is scalar, set the local result to None.
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# 2. if the return type is Tensor or List[Tensor], return empty
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# tensor(s) with correct dtype.
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spec = output_sharding.output_spec
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ret_list = op_info.schema.op._schema.returns
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if spec is None:
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# For a scalar return type, the non-participating device has None
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# as its local result
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local_results = None
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else:
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def default_tensor(spec: DTensorSpec) -> torch.Tensor:
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if spec.tensor_meta is not None:
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shape = spec.tensor_meta.shape
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dtype = spec.tensor_meta.dtype
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if len(shape) == 0:
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# scalar tensor
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return torch.zeros((), dtype=dtype)
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else:
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# non-scalar tensor
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return torch.tensor([], dtype=dtype)
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else:
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raise RuntimeError(f"{spec} has no tensor metadata.")
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if isinstance(spec, DTensorSpec):
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# return a Tensor value
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local_results = default_tensor(spec)
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elif isinstance(spec, Sequence):
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# return a List[Tensor] value
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local_results = [
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default_tensor(s) if s is not None else None for s in spec
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]
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assert isinstance(local_results, List)
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if None in local_results:
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ret_type = str(ret_list[0].type)
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raise NotImplementedError(
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f"return type {ret_type} in DTensor op is not supported"
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)
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if output_sharding.output_spec is None:
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if op_call == aten.equal.default:
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# For equal operator, The local results from all devices should be all-gathered
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# and a reduce op (AND) will be performed on the list of results to ensure SPMD
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# execution. We can extend this for more ops if necessary.
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obj_list = [None for _ in range(dist.get_world_size())]
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dist.all_gather_object(obj_list, local_results) # type: ignore[possibly-undefined]
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obj_list = list(filter(lambda x: x is not None, obj_list))
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# perform reduce on the collection with AND op
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local_results = functools.reduce(operator.and_, obj_list, True)
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if _is_inplace_op(op_call):
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# inplace op should return self instead of re-wrapping
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if output_sharding.output_spec is not None:
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return args[0]
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else:
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return None
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elif _is_out_variant_op(op_call):
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# out variant could possibly have multiple out args (i.e. lu_unpack.out)
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output_specs = (
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(output_sharding.output_spec,)
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if not isinstance(output_sharding.output_spec, tuple)
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else output_sharding.output_spec
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)
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out_dts = []
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spec_idx = 0
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for argument in op_call._schema.arguments:
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if argument.is_out:
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out_dt = cast(dtensor.DTensor, kwargs[argument.name])
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out_dt._spec = cast(DTensorSpec, output_specs[spec_idx])
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out_dts.append(out_dt)
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spec_idx += 1
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assert len(out_dts) >= 1, "out variant should have at least one out arg"
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return tuple(out_dts) if len(out_dts) > 1 else out_dts[0]
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else:
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return self.wrap(local_results, output_sharding.output_spec) # type: ignore[possibly-undefined]
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@staticmethod
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def redistribute_local_args(
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op_info: OpInfo,
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suggested_input_schema: OpSchema,
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) -> None:
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# NOTE: it's very rare that we need to reshard kwargs so we intentionally skip it
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if op_info.args_tree_spec is not None:
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flatten_args_schema_to_reshard = tuple(
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pytree.tree_leaves(suggested_input_schema.args_schema)
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)
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else:
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flatten_args_schema_to_reshard = suggested_input_schema.args_schema
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new_local_args: List[object] = []
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for i, arg_spec in enumerate(op_info.flat_args_schema):
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reshard_arg_spec = flatten_args_schema_to_reshard[i]
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if isinstance(arg_spec, DTensorSpec):
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local_tensor = cast(torch.Tensor, op_info.local_args[i])
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if arg_spec != reshard_arg_spec:
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resharded_local_tensor = redistribute_local_tensor(
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local_tensor, arg_spec, reshard_arg_spec
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)
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new_local_args.append(resharded_local_tensor)
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else:
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new_local_args.append(local_tensor)
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else:
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new_local_args.append(reshard_arg_spec)
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op_info.local_args = tuple(new_local_args)
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def unwrap_to_op_info(
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self,
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op_call: torch._ops.OpOverload,
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args: Tuple[object, ...],
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kwargs: Dict[str, object],
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) -> OpInfo:
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# get runtime schema info to determine whether to use pytree to flatten inputs
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runtime_schema_info = self.sharding_propagator.op_to_schema_info.get(
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op_call, None
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)
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if runtime_schema_info is not None and runtime_schema_info.needs_pytree:
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# flatten args/kwargs when op says necessary
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tree_args, args_spec = pytree.tree_flatten(args)
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args_list: Sequence[object] = tree_args
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else:
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args_list, args_spec = args, None
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args_schema: List[object] = []
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kwargs_schema: Dict[str, object] = {}
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local_args: List[object] = []
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local_kwargs: Dict[str, object] = {}
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mesh: Optional[DeviceMesh] = None
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for arg in args_list:
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if isinstance(arg, dtensor.DTensor):
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args_schema.append(arg._spec)
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local_args.append(arg._local_tensor)
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if mesh is not None:
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if mesh != arg.device_mesh:
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raise NotImplementedError(
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f"{op_call}: DTensor does not support cross-mesh operation yet!"
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f"Got meshes: {mesh} {arg.device_mesh}"
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)
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else:
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mesh = arg.device_mesh
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elif isinstance(arg, torch.Tensor):
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mesh = mesh or try_find_mesh_from_args(op_call, args_list)
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args_schema.append(
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self._try_replicate_spec_for_scalar_tensor(op_call, arg, mesh)
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)
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local_args.append(arg)
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else:
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args_schema.append(arg)
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local_args.append(arg)
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for k, v in kwargs.items():
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if isinstance(v, dtensor.DTensor):
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kwargs_schema[k] = v._spec
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local_kwargs[k] = v._local_tensor
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if mesh is not None:
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if mesh != v.device_mesh:
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raise NotImplementedError(
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f"{op_call}: DTensor does not support cross-mesh operation yet!"
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)
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else:
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mesh = v.device_mesh
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elif isinstance(v, torch.Tensor):
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mesh = mesh or try_find_mesh_from_args(op_call, args_list)
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kwargs_schema[k] = self._try_replicate_spec_for_scalar_tensor(
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op_call, v, mesh
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)
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local_kwargs[k] = v
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else:
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kwargs_schema[k] = v
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local_kwargs[k] = v
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assert mesh is not None, f"found no DeviceMesh from dtensor args for {op_call}!"
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op_info = OpInfo(
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mesh,
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OpSchema(
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op_call,
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pytree.tree_unflatten(args_schema, args_spec)
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if args_spec
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else tuple(args_schema),
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kwargs_schema,
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schema_info=runtime_schema_info,
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),
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args_schema,
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tuple(local_args),
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local_kwargs,
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args_spec,
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)
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return op_info
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@staticmethod
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def wrap(res: object, spec: OutputSpecType) -> object:
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if isinstance(res, torch.Tensor):
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if spec is not None:
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assert isinstance(
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spec, DTensorSpec
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), f"output spec does not match with output! Expected DTensorSpec, got {spec}."
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return dtensor.DTensor(res, spec, requires_grad=res.requires_grad)
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else:
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# if output does not have a DTensorSpec due to specific ops, it must be a scalar tensor
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assert res.ndim == 0, "output tensor should be scalar!"
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return res
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elif isinstance(res, (list, tuple)):
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assert spec is not None and isinstance(
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spec, (list, tuple)
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), f"output spec does not match with output! Expected list/tuple, got {spec}."
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res_list = []
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for e, s in zip(res, spec):
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res_list.append(OpDispatcher.wrap(e, s))
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return tuple(res_list) if isinstance(res, tuple) else res_list
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else:
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# if the res contains only non tensor values (i.e. int/float/none), we simply return it
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# without rewrapping to DTensor.
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return res
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def _try_replicate_spec_for_scalar_tensor(
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self,
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op_call: torch._ops.OpOverload,
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tensor_arg: torch.Tensor,
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mesh: "DeviceMesh",
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) -> DTensorSpec:
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# util function to produce a replicate spec for a scalar tensor arg/kwarg
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if tensor_arg.numel() == 1 and tensor_arg.ndim == 1:
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warnings.warn(
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"Found a non-scalar tensor with numel=1 and ndim!=0, "
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"we are implicitly creating a replicated DTensor for it. "
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"However, please consider changing it to a scalar tensor "
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"or explicitly create a DTensor under distributed enviroment."
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)
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if tensor_arg.numel() == 1 or self._allow_implicit_replication:
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# scalar tensor can be safely treated as replicated
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replication_spec = DTensorSpec(
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mesh,
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(Replicate(),) * mesh.ndim,
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tensor_meta=TensorMeta(
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shape=tensor_arg.shape,
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stride=tensor_arg.stride(),
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dtype=tensor_arg.dtype,
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),
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)
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else:
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raise RuntimeError(
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f"{op_call}: got mixed torch.Tensor and DTensor, need to convert all"
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" torch.Tensor to DTensor before calling distributed operators!"
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)
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return replication_spec
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